The increasing call for community-level evaluation of "combination HIV prevention" (packages of our best evidence-based HIV prevention interventions) has brought to the forefront the need to develop novel methods of both evaluating these combinations of interventions in aggregate and estimating the relative contributions of individual components of the combined intervention. However, such comprehensive methods do not exist. The recent development of the concept of Community Viral Load (CVL), presents a significant opportunity to evaluate T2 translational research (bedside-to-community) at the community level. CVL is an aggregate biologic measure of HIV-1 viral load from surveillance data that serves as a population-level marker of treatment mediated HIV virologic suppression and HIV transmission risk. We propose to develop novel research evaluation methods to assess the relative contribution of intervention components within a combination intervention by bringing together two developing innovations: 1) computational biology modeling of community viral load, and 2) multi-level process pathway analysis of program inputs, process measures of intervention components, and observed changes in CVL.
The specific aims are:
Aim 1 - Computational Biology Modeling of Community Viral Load (CVL):
In Aim 1 we will develop and test a computational biology model of CVL for San Francisco based on existing surveillance and clinic data.
Aim 2 - Multi-Level Process Path Analysis Methods Development:
In Aim 2 we will develop methods for the evaluation of change in CVL in response to multi-level/combination HIV prevention interventions by combining data obtained from CVL modeling in Aim 1 and hypothetical process measures of intervention components that could be observed in a combination HIV prevention intervention. We will utilize Structured Equation Modeling (SEM) and path analysis on simulated CVL data sets to estimate the relative contribution of intervention elements and estimate the sensitivity and specificity of competing analytic approaches.
Aim 3 - Empirical Data Testing:
In Aim 3 we will beta-test the best candidate process path model identified in Aim 2 using empirical data from a community-level combination HIV prevention intervention in San Francisco and estimate the performance of the methods. Significance: The proposed development of these urgently needed methods for evaluation of combination HIV prevention interventions has the promise of informing the science of community-level intervention assessment and will establish the feasibility of combining innovative CVL measurement with SEM process pathway analysis. Public Health Significance: Ultimately, the development of these methods to evaluate multi- level/combination prevention interventions at the community level will provide public health policy makers with an important tool to assess the community level effectiveness of these potentially high impact interventions and to assess which components merit implementation resource allocation.
This research will develop new methods to evaluate community-level combination HIV prevention (packages of our best evidence-based HIV prevention interventions). These methods will provide public health policy makers with an important tool to assess the community level effectiveness of these potentially high impact combination HIV prevention interventions and to assess which components merit implementation and to help allocate prevention resources.